Color perception is a psychological and physiological phenomenon that involves three elements: light, object and observer. Color changes as light, medium (i.e., paper, monitor) and observer interact. Color may be perceived differently under different types of lighting. Light sources that affect color include incandescent and fluorescent light. The first makes color seem more red and orange while the second emphasizes green and yellow tones. Different types of media also affect color perception. Observers view images on paper using reflection. In other cases the medium can be transmissive or emissive. Transparencies are an example of a transmissive medium while a computer monitor is emissive. The third element in the phenomenon is the observer. Different people may see the same color slightly differently. In order to characterize color image quality, the interaction of these elements must be understood so that when colors are intended to be matched, i.e., monitor to printer, scanner to printer, etc., acceptable appearance results.
For automatic control systems spectral data is often used to represent color perception as a pattern of wavelengths that leave the object before being interpreted by a viewer. Spectral data defines color independent of observer influence. Spectrophotometers and calorimeters are sensing devices used to measure spectral data.
There are different ways of representing color. One way color is described consists of the following parameters: hue, lightness and saturation. Hue represents the actual color wavelength (red, blue, etc.), lightness corresponds to the white content while saturation captures the richness or amplitude in color. Another way of describing color uses the three dominant primary colors red, blue and green (RGB). By combining these primary colors, in different intensities, most colors visible to humans can be reproduced. Monitors and scanners use the additive RGB color process. Printers use the subtractive CMYK (cyan, magenta, yellow and black) color process based on light reflected from inks coated on a substrate. To the extent that the color representations described above fail to reproduce color predictably, it is because they are observer or device dependent.
In today's business and scientific world, color has become essential as a component of communication. Color facilitates the sharing of knowledge and ideas. Companies involved in the development of digital color print engines are continuously looking for ways to improve the accuracy and total image quality of their products. One of the elements that affects image quality is the ability to consistently produce the same quality image output on a printer from one day to another, from one week to the next, month after month. Colors on a printer tend to drift over time. The variations arise from changes in the properties of the marking process (toner tribo, photoreceptor charge, transfer variations, temperature, humidity, and other marking and environmental factors). Maintaining an accurate printer operation requires regular recalibration, usually a time consuming, and accordingly undesirable task of sampling large numbers of test patches which are additionally interpolated to construct the CRD describing the printer transforming process. As electronic marketing has placed more importance on the accurate representation of merchandise in illustrative print or display media, consistent and accurate printer operation becomes more important, and regular recalibrating becomes more necessary.
Customers will usually specify their color demands in a device independent color space as part of a page description language (PDL). L*, a*, b* are the independent space representations of the CIE (Commission Internationale de L'éclairage) for color standards utilized in the functional modeling of these color demands. L* defines lightness, a* corresponds to the red/green value and b* denotes the amount of yellow/blue. Accurate transformation between L*, a*, b* representations and CMYK representations and vise versa are the principal applications of the subject invention.
When customers specify colors in device independent color coordinates, it is incumbent upon the printing/display device to determine which combination of available colorants (typically cyan, magenta, yellow and black) will yield the colors specified by the customer. This determination is frequently embodied in a look-up table (LUT) called the color rendition dictionary (CRD). Calculation of the CRD involves making patches in the printer's input space and examining the output thus yielding the forward transfer function which is then inverted to give the recipes for making the colors available in the printer's gamut.
These recipes are typically formulated using a large number of sampled data patches that are then interpolated to describe the transformation between the input and output spaces of the printing device. Because of the nature of the printer, these recipes are non-linear, time varying functions and thus are prone to inaccuracies if not updated at least occasionally. The updating process is tedious for customers and service representatives and is thus performed infrequently. If the process could be streamlined, it might be performed more frequently leading to improved color accuracy. With proper color calibration, customers could specify colors at document creation time and have an increased assurance that the printed colors would “match” (i.e. appear similar to) the desired colors.
The input space (domain) describes all the possible ways of mixing the three printer colorants (for example, cyan, magenta, and yellow but many other triplets are possible.) Typically the halftone density of a colorant is specified by an 8 bit integer (a whole number between 0 and 255). For the 3-space of inputs, the domain of the transformation consists of a three dimensional cube, 255 units on a side with one corner at the origin. The co-domain space of the transformation is the color gamut of the printer, the three dimensional volume that indicates all the L*, a*, b* values which are accessible by mixing the three colorants. This co-domain volume is of irregular shape and represents a sampled non-linear function of the input space. Since customers specify their desired colors in this space, the problem is to accurately print/display them and therefore derive the reverse transformation by inversion of the functional relationship between the domain and co-domain spaces detailed above.
No accurate functional representation of this transformation exists. The situation is complicated by the fact that the relationship between the digital inputs (domain) and the color outputs (co-domain) of the printer is not fixed for all time. If it were, the calibration could be performed once and always yield correct results. The variations in the appropriate calibration are due to variations in the printer's transfer function. Thus the transformation is at the same time non-linear (it requires a complex representation) and dynamic (varying in time).
On-line model prediction is also known as “system identification” in automatic controls literature. It is the terminology used for the process of characterizing a given system under control. Characterization of the system can be done in two ways; non-parametric and parametric. In non-parametric system identification, the profile of the device can be measured by printing specific target colors as specified by the ICC standards. This profile is used as it is (without constructing any model of the device) while making rendering decisions/viewing of the customer colors on the monitor. This is a one time measurement and does not use the historical information to construct any model. Whereas in the parametric system identification, target colors can be printed part of the banner sheet/header sheet or else the target colors can be extracted from the customer image and measured either by measuring straight from the output image or by rendering a subset of customer colors as target color patches. The intention in the parametric system identification is to adjust the parameters of the model and refine it over time by using past and present color data so that the model is accurate.
There is a continuing need for improved on-line modeling and convenient calibrating of color printing devices. Current needs are better served with model processing in a way that accurate parameters of a parametric model can be quickly identified through an interactive computation scheme. By “homeomorphism” in the context of this subject invention is meant the properties of the functions that are chosen to describe the transformations between sub-spaces in corresponding domains and co-domains where a particular parametric model defines the transformation from a domain value to a co-domain value. The homeomorphic transformations concerning a color marking device operation comprise the transformation function for data value conversion between the domain and co-domain. The subject invention deals with altering the defining boundary space of each of a set of sub-spaces, so that the parametric models associated with each sub-space are varied to a set which cumulatively most accurately estimates the non-linear printer transforming process, and further can be updated or recalibrated much more easily than could be done in the past.
The subject invention exploits a key enabling factor for these operational advantages by constructing and maintaining a current model of the reproducing device operation (also known as the device profile or characteristic, input-output model of reproducible color). The new and improved method constructs a dynamic model by segregating color spaces into a set of homeomorphisms each having an associated parametric model. When a customer has a need to accurately match the colors displayed or printed on various output devices, such as monitors (CRT, LCD, etc.) and printers (xerographic, ink jet, ionographic, etc.), he/she can recalibrate the existing model with a minimal number of new data patches and avoid having to build an entire new model and corresponding CRD.